Nonlinear Gaussian Filtering Theory Algorithms And Applications
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| Author | : Huber, Marco |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 302 |
| Release | : 2015-03-11 |
| Genre | : Electronic computers. Computer science |
| ISBN | : 3731503387 |
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems.
| Author | : Marco Huber |
| Publisher | : |
| Total Pages | : 294 |
| Release | : 2020-10-09 |
| Genre | : Mathematics |
| ISBN | : 9781013280696 |
By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
| Author | : Janya-anurak, Chettapong |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 248 |
| Release | : 2017-04-04 |
| Genre | : Electronic computers. Computer science |
| ISBN | : 3731506424 |
In this work, the Uncertainty Quantification (UQ) approaches combined systematically to analyze and identify systems. The generalized Polynomial Chaos (gPC) expansion is applied to reduce the computational effort. The framework using gPC based on Bayesian UQ proposed in this work is capable of analyzing the system systematically and reducing the disagreement between the model predictions and the measurements of the real processes to fulfill user defined performance criteria.
| Author | : Jauch, Jens |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 264 |
| Release | : 2024-03-01 |
| Genre | : |
| ISBN | : 3731513323 |
This work describes a method for weighted least squares approximation of an unbounded number of data points using a B-spline function. The method can shift the bounded B-spline function definition range during run-time. The approximation method is used for optimizing velocity trajectories for an electric vehicle with respect to travel time, comfort and energy consumption. The trajectory optimization method is extended to a driver assistance system for automated vehicle longitudinal control.
| Author | : Simo Särkkä |
| Publisher | : Cambridge University Press |
| Total Pages | : 255 |
| Release | : 2013-09-05 |
| Genre | : Computers |
| ISBN | : 110703065X |
A unified Bayesian treatment of the state-of-the-art filtering, smoothing, and parameter estimation algorithms for non-linear state space models.
| Author | : Laura Leal-Taixé |
| Publisher | : Springer |
| Total Pages | : 763 |
| Release | : 2019-01-22 |
| Genre | : Computers |
| ISBN | : 303011015X |
The six-volume set comprising the LNCS volumes 11129-11134 constitutes the refereed proceedings of the workshops that took place in conjunction with the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.43 workshops from 74 workshops proposals were selected for inclusion in the proceedings. The workshop topics present a good orchestration of new trends and traditional issues, built bridges into neighboring fields, and discuss fundamental technologies and novel applications.
| Author | : Bin Jia |
| Publisher | : CRC Press |
| Total Pages | : 198 |
| Release | : 2019-04-25 |
| Genre | : Mathematics |
| ISBN | : 1351757407 |
Grid-based Nonlinear Estimation and its Applications presents new Bayesian nonlinear estimation techniques developed in the last two decades. Grid-based estimation techniques are based on efficient and precise numerical integration rules to improve performance of the traditional Kalman filtering based estimation for nonlinear and uncertainty dynamic systems. The unscented Kalman filter, Gauss-Hermite quadrature filter, cubature Kalman filter, sparse-grid quadrature filter, and many other numerical grid-based filtering techniques have been introduced and compared in this book. Theoretical analysis and numerical simulations are provided to show the relationships and distinct features of different estimation techniques. To assist the exposition of the filtering concept, preliminary mathematical review is provided. In addition, rather than merely considering the single sensor estimation, multiple sensor estimation, including the centralized and decentralized estimation, is included. Different decentralized estimation strategies, including consensus, diffusion, and covariance intersection, are investigated. Diverse engineering applications, such as uncertainty propagation, target tracking, guidance, navigation, and control, are presented to illustrate the performance of different grid-based estimation techniques.
| Author | : Bullinger, Sebastian |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 194 |
| Release | : 2020-08-26 |
| Genre | : Computers |
| ISBN | : 373151012X |
"This work proposes a Multibody Structure from Motion (MSfM) algorithm for moving object reconstruction that incorporates instance-aware semantic segmentation and multiple view geometry methods. The MSfM pipeline tracks two-dimensional object shapes on pixel level to determine object specific feature correspondences, in order to reconstruct 3D object shapes as well as 3D object motion trajectories" -- Publicaciones de Arquitectura y Arte.
| Author | : Becker, Stefan |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 228 |
| Release | : 2020-12-02 |
| Genre | : Computers |
| ISBN | : 3731510383 |
This work addresses the problem of how to capture the dynamics of maneuvering objects for visual tracking. Towards this end, the perspective of recursive Bayesian filters and the perspective of deep learning approaches for state estimation are considered and their functional viewpoints are brought together.
| Author | : Pfrommer, Julius |
| Publisher | : KIT Scientific Publishing |
| Total Pages | : 210 |
| Release | : 2024-06-04 |
| Genre | : |
| ISBN | : 373151253X |
In dieser Arbeit wird ein Ansatz entwickelt, um eine automatische Anpassung des Verhaltens von Produktionsanlagen an wechselnde Aufträge und Rahmenbedingungen zu erreichen. Dabei kommt das Prinzip der Selbstorganisation durch verteilte Planung zum Einsatz. - Most production processes are rigid not only by way of the physical layout of machines and their integration, but also by the custom programming of the control logic for the integration of components to a production systems. Changes are time- and resource-expensive. This makes the production of small lot sizes of customized products economically challenging. This work develops solutions for the automated adaptation of production systems based on self-organisation and distributed planning.
